AI Algorithms Research Articles

Non-speech emotion recognition based on back propagation feed forward networks

Non-speech emotion recognition involves identifying emotions conveyed through non-verbal vocalizations such as laughter, crying, and other sound signals, which play a crucial role in emotional expression and transmission. This paper employs a nine-category discrete emotion model encompassing happy, sad, angry, peaceful, fearful, loving, hateful, brave, and neutral. A proprietary non-speech dataset comprising 2337 instances was utilized, with 384-dimensional feature vectors extracted. The traditional Backpropagation Neural Network (BPNN) algorithm achieved a recognition rate of 87.7% on the non-speech dataset. In contrast, the proposed Whale Optimization Algorithm - Backpropagation Neural Network (WOA-BPNN) algorithm, applied to a self-made non-speech dataset, demonstrated a remarkable accuracy of 98.6%. Notably, even without facial emotional cues, non-speech sounds effectively convey dynamic information, and the proposed algorithm excels in their recognition. The study underscores the importance of non-speech emotional signals in communication, especially with the continuous advancement of artificial intelligence technology. The abstract thus encapsulates the paper’s focus on leveraging AI algorithms for high-precision non-speech emotion recognition.

Novel Approach to Design a Model for Software Effort Estimation Using Linear Regression

Estimation of an effort is necessary to decide the time and cost needed for the completion of the project. Making judgments with a high degree of uncertainty is a serious issue in the field of software engineering. Software quality forecasting calls for highly precise technologies and expert knowledge. The forecast of software effort based on past data from software development metrics could, instead, be aided by AI-based predictive models, which have a high degree of accuracy. Using a linear regression model, we developed a software effort estimation model in this study to forecast this effort. A non-parametric linear regression approach based on K-Nearest Neighbors was used to create this statistical model (KNN). So, with a 76% coefficient of determination, our findings indicate the potential for applying AI algorithms to predict the software engineering work prediction problem.

Fostering Transformation: Unleashing the Power of Artifical Intelligence and Machine Learning in the Field of Radiation Oncology.

The article explores AI and ML's transformative potential in reshaping the radiation therapy landscape. The article navigates through the evolving field of radiation oncology, highlighting the constant influx of information facilitated by advanced imaging techniques. The technical scrutiny of AI's potential within radiation oncology is explored, contrasting definitions by Russell and Norvig with Goel's more insightful perspective. A detailed overview of the radiation therapy process, from diagnosis to follow-up, sets the stage for discussing the role of AI and ML. The utilities of AI in radiation oncology are dissected, emphasizing the reduction of clinical load through decision support systems, streamlined treatment planning, and the automated enhancement of radiation therapy. The article showcases various AI algorithms deployed in the workflow, their applications, and the promising results they offer. While acknowledging the challenges, including the opaque nature of AI and the critical need for clinical adoption, the article outlines criteria for evaluating AI tools in clinical settings. It stresses the importance of trust-building, transparency and overcoming challenges to harness AI's full potential in radiation oncology. In conclusion, the article advocates for a proactive integration of AI and ML, envisioning a future where these technologies empower radiation oncologists to enhance patient care, optimize workflows, and advance the field.

An Emerging Era Of Research In Agriculture Using AI

AI-driven precision agriculture, predictive analytics, robots, and market intelligence boost contemporary agriculture's production, efficiency, and sustainability. Precision agriculture, powered by AI algorithms, gives farmers detailed insights into crop health, soil conditions, and weather patterns for data-driven resource allocation and management. AI in agriculture's predictive analytics helps stakeholders forecast crop yields, market dynamics, and climate-related dangers, improving resilience and strategic planning. AI has great promise to solve agriculture's complicated problems. AI technologies allow computers to mimic human cognition and evaluate massive volumes of data to draw conclusions. AI can improve resource utilization, productivity, decision-making, and environmental effect in agriculture. AI-powered precision agriculture, crop monitoring, supply chain optimization, and market analysis are making agriculture more sustainable and resilient. To show AI's influence on farming, we explored precision agriculture, predictive analytics, robots, and supply chain optimization. Farmers may optimize resource usage, manage risks, and make data-driven choices using these tools, enhancing output, sustainability, and resilience.

Measuring the advertising value of personalised ads in smartphones based on consumer literacy and privacy concerns

Because of the widespread use of smartphones in everyday life and advances in data analysis technology, brands are increasingly focusing on advertising via smartphone while collecting customer data. Advertisements are made to reach customers through two types of data collection: the first is using customer online behaviour to show personalised ads, which is known as online behavioural advertising (OBA), and the second is hearing out customer media usage and providing personalised ads, which is known as synced advertising (SA). The question here is whether personalised ads are useful and project a positive image to customers, which are reliable. The purpose of this paper is to provide a knowledge of how OBA and SA work and why it is vital to address personalised adverts in smartphones that are powered by AI algorithms due to privacy concerns. The purpose of this empirical investigation is to determine is purchase intention is accelerated based on the advertising value of personalised adverts, their literacy, and privacy concerns and to test the effect of personalised advertisement value on purchase intention.

Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips.In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles.While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency.This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems.

Optimization and Performance Evaluation of Artificial Intelligence Algorithms in Game Design

This article aims to optimize artificial intelligence algorithms in game design to enhance game performance and player experience. Therefore, an innovative method called Dynamic Game AI Fitness Optimization Algorithm (DGAFOA) was proposed in the article, and its effectiveness was verified through experiments. In the experimental section, this article applies the DGAFOA algorithm to a typical role-playing game and compares it with traditional fixed parameter AI algorithms. The experimental results show that the DGAFOA algorithm exhibits significant advantages in key indicators such as game task completion rate and player satisfaction. Specifically, game AI using the DGAFOA algorithm can respond more quickly and accurately to player behavior, improving the overall smoothness and fun of the game. In addition, the DGAFOA algorithm also introduces ε- The greedy strategy balances exploration and utilization, effectively avoiding the problem of AI getting stuck in local optima. This enables game AI to maintain stable performance while still possessing the ability to explore new strategies, bringing players more surprises and challenges.

Interpretable AI models for judicial decisionmaking: beyond explicability towards legal due process

As AI algorithms are employed to apply legal rules in determining rights and obligations, questions related to the observance of due legal process arise. The development of opaque machine learning models, whose predictions cannot be satisfactorily explained, has spurred debates around the idea of explainability of AI models for decision-making. The article argues that (i) in relation to AI models for judicial decision-making, the standard of explainability, besides proving insufficient to meet the requirements for publicity and reasoning of judicial decisions, imposes a form of nakedness not required of human judges; and (ii) a more appropriate standard would be that of interpretable models for judicial decision-making, characterized as able to offer decisions that are referred to current law (legality), internally and externally coherent (consistency), and compatible with the decision of a human judge in a similar case.

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been placed at algorithm level rather than system level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI.

Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey

Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However, access to high-quality datasets is limited by the technical difficulties of data acquisition, and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with distributions similar to real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Therefore, in this article, we will review synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-style review article will provide comprehensive descriptions of non-imaging medical data synthesis, covering aspects such as algorithms, evaluations, limitations, and future research directions.

The potential of artificial intelligence in early diagnosis and personalized treatment: Advances and challenges in modern medicine

Carrying out this study is justified by its academic, scientific and social relevance, based on presenting technological advances involving artificial intelligence in healthcare. Therefore, the objective of this research focuses on highlighting the applications and benefits of Artificial Intelligence in Medicine. This study was carried out through an integrative literature review, with an exploratory approach, whose institute was limited to investigating, through already published articles, relevant information that answered the guiding question. Thus, data collection took place in scientific bases: SCIELO and LILACS. One of the main advantages of using AI in early diagnosis is the ability to process large volumes of data quickly and accurately, identifying patterns and signals that may be imperceptible to human healthcare professionals. This makes it possible to detect serious medical conditions early, such as cancer and heart disease, when treatment is most effective and the chances of recovery are greatest. Furthermore, AI can significantly contribute to personalized treatment, adapting therapeutic approaches based on the individual characteristics of each patient. By analyzing genetic data, medical history, and response to previous treatments, AI algorithms can help doctors develop treatment plans that are more effective and have fewer side effects.

AI Enabled MED Drone for Healthcare Application

The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.

Globe glider

Globe glider Tours and Travels aims to revolutionize the travel industry by offering a comprehensive platform that integrates seamless booking experiences, personalized trip planning, and cutting-edge travel recommendations. With an emphasis on leveraging advanced AI algorithms and machine learning techniques, Globe glider seeks to redefine the way travelers discover, plan, and embark on their journeys. Our platform will provide users with a holistic travel solution, from browsing destinations to booking accommodations, transportation, and activities, all tailored to their preferences and interests. Globe glider’s mission is the commitment to enhancing the traveler's experience through innovative technology and curated content. By harnessing vast datasets and employing sophisticated algorithms, our platform will deliver personalized recommendations for destinations, activities, and experiences based on each user's unique profile and past travel behavior. Additionally, Globeglider will offer real-time assistance and support throughout the travel journey, ensuring a seamless and stress-free experience from start to finish. In an ever-evolving travel landscape, Globe glider stands poised to empower travelers with the tools and resources needed to make informed decisions and create unforgettable experiences. With a focus on user-centric design, technological innovation, and unparalleled customer service, Globeglider aspires to become the go-to platform for travelers worldwide, facilitating seamless exploration, discovery, and adventure across the globe. Globeglider sets itself apart by prioritizing sustainability and responsible tourism practices. In an era where environmental conservation and ethical travel are paramount concerns, our platform will actively promote eco-friendly travel options, support local communities, and advocate for the preservation of cultural heritage sites. Through partnerships with eco-conscious suppliers and community-based tourism initiatives, we aspire to minimize the carbon footprint of travel while maximizing positive socio-economic impacts on host destinations. At the heart of GlobeGlider is a commitment to fostering a sense of exploration and connection among travelers. The application incorporates a user-centric approach, allowing individuals to customize their journeys based on preferences, interests, and travel goals. GlobeGlider not only streamlines the logistics of travel but also enhances the overall experience by offering curated suggestions, insider tips, and interactive maps. By combining convenience with a touch of adventure, GlobeGlider aims to empower users to embark on journeys that are not just destinations but transformative moments in their lives.

Predictive modeling of skin permeability for molecules: Investigating FDA-approved drug permeability with various AI algorithms.

The transdermal route of drug administration has gained popularity for its convenience and bypassing the first-pass metabolism. Accurate skin permeability prediction is crucial for successful transdermal drug delivery (TDD). In this study, we address this critical need to enhance TDD. A dataset comprising 441 records for 140 molecules with diverse LogKp values was characterized. The descriptor calculation yielded 145 relevant descriptors. Machine learning models, including MLR, RF, XGBoost, CatBoost, LGBM, and ANN, were employed for regression analysis. Notably, LGBM, XGBoost, and gradient boosting models outperformed others, demonstrating superior predictive accuracy. Key descriptors influencing skin permeability, such as hydrophobicity, hydrogen bond donors, hydrogen bond acceptors, and topological polar surface area, were identified and visualized. Cluster analysis applied to the FDA-approved drug dataset (2326 compounds) revealed four distinct clusters with significant differences in molecular characteristics. Predicted LogKp values for these clusters offered insights into the permeability variations among FDA-approved drugs. Furthermore, an investigation into skin permeability patterns across 83 classes of FDA-approved drugs based on the ATC code showcased significant differences, providing valuable information for drug development strategies. The study underscores the importance of accurate skin permeability prediction for TDD, emphasizing the superior performance of nonlinear machine learning models. The identified key descriptors and clusters contribute to a nuanced understanding of permeability characteristics among FDA-approved drugs. These findings offer actionable insights for drug design, formulation, and prioritization of molecules with optimum properties, potentially reducing reliance on costly experimental testing. Future research directions include offering promising applications in pharmaceutical research and formulation within the burgeoning field of computer-aided drug design.

Transforming Management Decision - Making: Role of ChatGPT

This study explores how ChatGPT, an advanced artificial intelligence language model, has revolutionized management decision-making processes and clarifies how it will influence corporate governance going forward. This study investigates how ChatGPT improves decisionmaking efficiency, accuracy, and strategic foresight through an analysis of current trends and breakthroughs in AI technology, supported by empirical data from real-world applications. It also explores the dynamic interaction between AI algorithms and human expertise, emphasizing the collaborative potential of decision-making frameworks. The examination addresses organizational, ethical, and legal issues in addition to highlighting the advantages and difficulties of using ChatGPT into managerial practices. This study adds to a more nuanced knowledge of ChatGPT’s impact on managerial decision-making and offers practical advice to companies looking to successfully use AI to navigate the challenges of the future.

Design and development of intelligent unmanned construction system for open-pit mine

China’s huge mineral outputs support the national economy and people’s livelihood. However, the mining operation is rough and the safety accident happens frequently. Consequently, the demand for unmanned mining operations is urgent, which is of great significance to safe production. Facing the complex operation scenarios of open-pit mines, an unmanned construction system framework is proposed based on intelligent construction machinery. On this basis, an unmanned excavation subsystem is designed and developed based on the hydraulic excavator, and an unmanned transportation subsystem is designed and developed based on the mining truck. Equipped with advanced sensors and controllers, the unmanned excavation subsystem and the unmanned transportation subsystem possess sensing, decision-making, and control abilities by using AI algorithms. The experiment of the unmanned construction systems is successfully conducted on the open-pit mine test site. This study provides equipment and technical support for improving the intelligent level of mine construction and ensuring safe production for the open-pit mine.

Neuromarketing Insights Enhanced by Artificial Intelligence

The intersection of neuromarketing and artificial intelligence is examined in this research, along with the significant implications for transforming marketing tactics. By using neuroscience methods to identify subconscious reactions to marketing stimuli, neuromarketing sheds light on consumer behaviour. The integration of AI enhances neuromarketing research by effectively analysing neurodata and detecting significant patterns. When combined, they allow for marketing initiatives that are emotionally compelling, targeted, and personalised. However, ethical issues pertaining to bias in AI algorithms, customer privacy, and societal ramifications need to be taken into account. Businesses must adopt this multidisciplinary strategy if they want to stay ahead in the increasingly competitive market. This study advocates for responsible and ethical use in marketing practices while highlighting the transformational potential of utilising AI technologies with neuroscience findings.

Balancing Act: The Dual Impacts of AI and Healthcare Algorithms on Patient Welfare in Telemedicine

Balancing Act: The Dual Impacts of AI and Healthcare Algorithms on Patient Welfare in Telemedicine

Transforming Neurology Care Delivery Through a Population Health Data Strategy.

With more than 30% of global data originating from health care, deriving usable insights that improve health requires population health analytics. In neurology, data-driven approaches have grown in significance because of digital health records and advanced analytics. A vital aspect of this evolution is adopting a population health data strategy (PHDS). Crafting a tailored PHDS for neurology involves cataloging data points and measures spanning demographics, clinical history, genetics, and social determinants. Neurologic outcomes include mortality rates, functional and cognitive abilities, and imaging results. A robust strategy relies on interoperability, advanced analytics, and transparent AI algorithms. Neurology is embracing data-driven health care. The PHDS synthesizes diverse patient data to provide personalized care. It includes a wide range of outcome measures to address neurologic complexities. Advanced analytics and collaboration among neurologists, data scientists, and business leaders uncover hidden patterns and promote outcome-driven medicine in the 21st century.

Data-Driven Agriculture: Empowering Farmers with IoT and Analytics

Abstract: The agriculture industry faces daunting challenges like climate change, water scarcity, and pest outbreaks, hindering farmers from meeting the demands of a growing population. To tackle these issues, this project proposes a smart farming platform integrating IoT devices, data analytics, and a user-friendly interface. It aims to optimize crop management, boost yields, and conserve resources. The platform includes sensor nodes and IoT devices gathering data on soil moisture, temperature, humidity, light intensity, and atmospheric conditions. It leverages predictive analytics for real-time insights on crop health, growth patterns, and potential threats like diseases and pests. Early detection mechanisms using image recognition and AI algorithms will be employed. Weather forecasting integration helps in decision-making for agricultural operations. Crop-specific recommendations based on local conditions are provided. The platform offers a dashboard interface for real-time monitoring, notifications, and remote control of automated systems. Historical data analysis aids in identifying patterns and trends for future crop cycles.